Design Of Photovoltaic Plant Research Articles

Design Of PV Plant to Improve a Voltage Profile Of 11 KV Grid in The South West of Libya

The use of photovoltaic (PV) system for electricity generation is growing up in the last decades. Thesouthern part of Libya suffers from voltage fluctuation and intermittence of electricity during thesummer time. The aim of this paper is firstly to study and analysis a distribution grid of small villagelocated in the southern west region of Libya. We analyzed the existed 11KV distribution overheadline supplied electricity of the village called Takarkibah using Power-World Simulator in order tointegrate it with PV plant. The results obtained show that injection of PV power about 70 % of thegrid power capacity gives good grid performance. After that, we use SAM simulation program todesign PV plant to improve the voltage profile in the distribution grid. Six designs (scenarios) aresimulated using different solar modules and inverters. The simulation results obtained from SAMillustrated the power capacity, energy yield, and losses of the designed PV plant.

Impact of the tilt angle, inverter sizing factor and row spacing on the photovoltaic power forecast accuracy

The growth of the installed photovoltaic (PV) capacity calls for accurate power forecasts, which are commonly calculated from irradiance forecasts using physical model chains. This study analyses the impact of the PV plant design parameters on the additional error of the irradiance-to-power conversion using three approaches. First, PV power curves are plotted for different design parameters to illustrate the amplification of errors and to enhance the understanding of the presented phenomena. Second, six forecast accuracy metrics are calculated as a function of the design parameters for the irradiance observation and forecast data of ten stations from five climate zones and two forecast providers. Third, the Pareto-optimal tradeoff between the annual energy production and the absolute forecast errors are obtained by the NSGA-II multi-objective optimization algorithm. The results reveal that the design parameters highly influence the PV power forecast errors, which is important to consider in all studies presenting PV power forecast verification. A tilt angle of 45° increases the error metrics up to 49% compared to a horizontal plane, and undersizing the inverters by a 1.5 sizing factor lead to an average error decrease up to 25%. Including the expected forecast errors as an objective during their design optimization process is important to maximize the net revenues of PV plants, especially if the unit imbalance costs are more than 50% of the electricity prices.

Towards better performances for a novel rooftop solar PV system

Solar photovoltaic (PV) systems are used worldwide for clean production of electricity. Photovoltaic simulation tool serve to predict the amount of energy generated by the PV solar array structure. This paper presents the photovoltaic system installed on the rooftop of the G.D. Naidu Block at Vellore Institute of Technology (Vellore, India). A novel PV plant design is developed here in order to improve the energetic efficiency of an existing PV system. The effectiveness of proposed design is evaluated over an entire year using the PVsyst v6.70 software, which works on accurate plant specifications. For this purpose, Metronome 7.1 weather data sets of ambient temperature and radiation from PVsyst database are used for the investigation. The cost of the proposed PV system and the required payback period are analyzed as well. Simulation results demonstrate the superiority of the proposed PV system design over the existing one in terms of the amount of electric energy injected in the grid, energy conversion efficiency, and reductions in CO2/SO2/NO emissions. Performance ratio of proposed design (Design 2) is 0.791 whereas the existing design (Design 1) is only 0.704. Design 2 provides 40 MWh more energy to grid than Design 1 due to reducing shading losses. The daily system energy generated for Design 2 is maximum (in particular, 26–29% higher than for Design 1) between March and May, when the sun is brightest and directly above our head. Shading analysis carried out for both designs revealed that the existing Design 1 has more shading loss while the proposed Design 2 may reduce this loss by about 11–13%, which results in a better efficiency of energy production. The article also documents significant emission reduction and cost analysis calculation for the proposed Design 2.

Identifiability Evaluation of Crucial Parameters for Grid Connected Photovoltaic Power Plants Design Optimization

This paper aims to assess the impact of different key factors on the optimized design and performance of grid connected photovoltaic (PV) power plants, as such key factors can lead to re-design the PV plant and affect its optimum performance. The impact on the optimized design and performance of the PV plant is achieved by considering each factor individually. A comprehensive analysis is conducted on nine factors such as; three objectives are predefined, five recent optimization approaches, three different locations around the world, changes in solar irradiance, ambient temperature, and wind speed levels, variation in the available area, PV module type and inverters size. The performance of the PV plant is evaluated for each factor based on five performance parameters such as; energy yield, sizing ratio, performance ratio, ground cover ratio, and energy losses. The results show that the geographic location, a change in meteorological conditions levels, and an increase or decrease in the available area require the re-design of the PV plant. A change in inverter size and PV module type has a significant impact on the configuration of the PV plant leading to an increase in the cost of energy. The predefined objectives and proposed optimization methods can affect the PV plant design by producing completely different structures. Furthermore, most PV plant performance parameters are significantly changed due to the variation of these factors. The results also show the environmental benefit of the PV plant and the great potential to avoid green-house gas emissions from the atmosphere.

Novel Utility-Scale Photovoltaic Plant Electroluminescence Maintenance Technique by Means of Bidirectional Power Inverter Controller

Nowadays, photovoltaic (PV) silicon plants dominate the growth in renewable energies generation. Utility-scale photovoltaic plants (USPVPs) have increased exponentially in size and power in the last decade and, therefore, it is crucial to develop optimum maintenance techniques. One of the most promising maintenance techniques is the study of electroluminescence (EL) images as a complement of infrared thermography (IRT) analysis. However, its high cost has prevented its use regularly up to date. This paper proposes a maintenance methodology to perform on-site EL inspections as efficiently as possible. First, current USPVP characteristics and the requirements to apply EL on them are studied. Next, an increase over the automation level by means of adding automatic elements in the current PV plant design is studied. The new elements and their configuration are explained, and a control strategy for applying this technique on large photovoltaic plants is developed. With the aim of getting on-site EL images on a real plant, a PV inverter has been developed to validate the proposed methodology on a small-scale solar plant. Both the electrical parameters measured during the tests and the images taken have been analysed. Finally, the implementation cost of the solution has been calculated and optimised. The results conclude the technical viability to perform on-site EL inspections on PV plants without the need to measure and analyse the panel defects out of the PV installation.

Techno-economic optimization of grid-connected, ground-mounted photovoltaic power plants by genetic algorithm based on a comprehensive mathematical model

The increasing penetration of photovoltaic (PV) technology calls for the development of an effective method for optimization of grid-connected photovoltaic power plants. This paper presents a simultaneous optimization method of ten important design parameters of a PV plant, including the module power, inverter sizing, support structure dimensions, cable losses, module orientation and row spacing. A mathematical PV performance model taking into account the important effects and losses and an economic cost model were developed and presented in detail. The objective function is the internal rate of return and the optimization is performed by a genetic algorithm. The results show that the proposed models and method are capable to optimize the grid-connected PV plant and provide reliable results after a 6–7 min calculation time. The method was demonstrated in detail for a Hungarian location, including the losses and cost structure of the optimal plant configuration. The optimization was also performed for 5 additional sites around the world to assess the effect of location and meteorology. The impact of the decreasing PV module prices on the optimal design is calculated to identify the expected future trends in PV plant design. The presented optimization method can be utilized to facilitate the optimal design of commercial PV plants and for research purposes.

Improving Solar Power PV Plants Using Multivariate Design Optimization

The proliferation of photovoltaic (PV) installations across the globe has accelerated dramatically in the past decade covering home, rural, mobile, industrial, and utility-scale applications. In all these cases, improving payback time and energy production for PV installations is a very complex design tradeoff that involves multiple variables such as irradiance fluctuations, inverter efficiency, operating temperature variation, and PV panel type. In this paper, a detailed multivariate study of PV plant design is presented, resulting in an improved technique to increase the potential benefits of solar plants with lower capital costs. This new approach includes detailed consideration of the probabilistic hourly temperature and solar irradiation profile of the installation site, the efficiencies and operating areas of different grid-tie inverters, and detailed models of different PV modules in the optimal design process. The harvested energy, total costs, and payback time are the objective functions in this approach, while the number of series and parallel panels, the tilt angle, and inverter topology and PV module type are determined from a list of possible candidates. The optimization process is implemented for a sample system, and the results are compared to both a traditional and design software approach. It is seen that by applying the proposed approach with lower capital costs, the harvested energy, financial benefits, and the payback time can be improved by 9.3%, 1%, and 6.95%, respectively. Several case studies are then presented to investigate the sensitivity and robustness of the design with regard to the ambient temperature variation, solar irradiation fluctuation, and available surface area for PV module installation.

Optimal Design of Photovoltaic Systems Using High Time-Resolution Meteorological Data

The installation of photovoltaic (PV) plants has been expanding rapidly across the world during the last years. In this paper, a methodology for the design optimization of PV plants is presented, which, in contrast to the conventional PV plant design approaches, is suitable to be executed using high time-resolution (i.e., 1-min-average) values of the meteorological input data. Due to the nonlinear operation of the devices comprising a PV plant, this allows for the accurate estimation of the PV plant performance during its operational lifetime period. A parallel processing-based implementation of genetic algorithms has been employed, which, compared to the serial execution, provides the ability to accomplish the proposed optimal design procedure in a considerably shorter time interval. The design optimization results confirm that the proposed method successfully accounts for both the meteorological conditions and the operational characteristics of the PV plant components and incorporates their impact on the PV plant energy production and cost in the design process. Thus, the proposed optimization method allows for optimum design of PV systems, which will provide maximum economic profit during their lifetime period.

Evolutionary algorithms for the design of grid-connected PV-systems

The sale of electric energy generated by photovoltaic (PV) plants has attracted much attention in recent years. The installation of PV plants aims to obtain the maximum benefit of captured solar energy. The current methodologies for planning the design of the different components of a PV plant are not completely efficient. This paper addresses the optimization of the design of PV plants with solar tracking, which consists of the optimization of the variables that make up the PV plant to obtain the minimum electric (Joule) losses possible. These variables are the size and distribution of solar modules in the solar tracker, the distribution of the solar trackers in the field and the choice of inverter. Evolutionary algorithms (EAs) are adaptive methods based on natural evolution that may be used for searching and optimization. Four different EAs have been used for optimizing the design of PV plants: steady-state genetic algorithm, generational genetic algorithm, CHC algorithm and DE algorithm. In order to test the performance of these algorithms we have used different proposed fields to mount PV plants. The results obtained show that EAs, and specifically DE with rand mutation schemes, are promising techniques to optimize design of PV plants. Furthermore, the results are contrasted with nonparametric statistical tests to support our conclusions.

The design of photovoltaic plants: An optimization procedure

In this paper we discuss a simple analytical method which allows one to predict the fraction of the load covered by a photovoltaic plant as a function of the dimensions of its components (i.e. area of the photovoltaic array and battery storage capacity), of the meteorological parameters and of the user's load. We then use this method to perform an economic analysis for standalone plants and for fuel-assisted plants.